A model to predict risk of stroke in middle-aged adults with type 2 diabetes generated from a nationwide population-based cohort study in Korea

Physical Activity and Risk of Major Diabetes-Related Complications in Individuals With Diabetes: A Systematic Review and Meta-Analysis of Observational Studies

BACKGROUND

Physical activity is a cornerstone in diabetes management; however, evidence synthesis on the association between physical activity and long-term diabetes-related complications is scarce.

PURPOSE

To summarize and evaluate findings on physical activity and diabetes-related complications, we conducted a systematic review and meta-analysis.

DATA SOURCES

We searched PubMed, Web of Science, and the Cochrane Library for articles published up to 6 July 2021.

STUDY SELECTION

We included prospective studies investigating the association between physical activity and incidence of and mortality from diabetes-related complications, i.e., cardiovascular disease (CVD), coronary heart disease, cerebrovascular events, heart failure, major adverse cardiovascular events, and microvascular complications such as retinopathy and nephropathy, in individuals with diabetes.

DATA EXTRACTION

Study characteristics and risk ratios with 95% CIs were extracted. Random-effects meta-analyses were performed, and the certainty of evidence and risk of bias were evaluated with use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tools.

DATA SYNTHESIS

Overall, 31 studies were included. There was moderate certainty of evidence that high versus low levels of physical activity were inversely associated with CVD incidence, CVD mortality (summary risk ratio 0.84 [95% CI 0.77, 0.92], n = 7, and 0.62 [0.55, 0.69], n = 11), and microvascular complications (0.76 [0.67, 0.86], n = 8). Dose-response meta-analyses showed that physical activity was associated with lower risk of diabetes-related complications even at lower levels. For other outcomes, similar associations were observed but certainty of evidence was low or very low.

LIMITATIONS

Limitations include residual confounding and misclassification of exposure.

CONCLUSIONS

Physical activity, even below recommended amounts, was associated with reduced incidence of diabetes-related complications.

Current Trends of Big Data Research Using the Korean National Health Information Database

Recently, medical research using big data has become very popular, and its value has become increasingly recognized. The Korean National Health Information Database (NHID) is representative of big data that combines information obtained from the National Health Insurance Service collected for claims and reimbursement of health care services and results obtained from general health examinations provided to all Korean adults. This database has several strengths and limitations. Given the large size, various laboratory data, and questionnaires obtained from medical check-ups, their longitudinal nature, and long-term accumulation of data since 2002, carefully designed studies may provide valuable information that is difficult to obtain from other forms of research. However, consideration of possible bias and careful interpretation when defining causal relationships is also important because the data were not collected for research purposes. After the NHID became publicly available, research and publications based on this database have increased explosively, especially in the field of diabetes and metabolism. This article reviews the history, structure, and characteristics of the Korean NHID. Recent trends in big data research using this database, commonly used operational diagnosis, and representative studies have been introduced. We expect further progress and expansion of big data research using the Korean NHID.

Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models

AIMS

To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability METHODS: A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations.

RESULTS

The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated.

CONCLUSIONS

There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.

Effect of Physical Activity on Cardiovascular Event Risk in a Population-Based Cohort of Patients with Type 2 Diabetes

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality among patients with type 2 diabetes (T2D). Physical activity (PA) is one of the few modifiable factors that can reduce this risk. The aim of this study was to estimate to what extent PA can contribute to reducing CVD risk and all-cause mortality in patients with T2D. Information from a population-based cohort including 26,587 patients with T2D from the Navarre Health System who were followed for five years was gathered from electronic clinical records. Multivariate Cox regression models were fitted to estimate the effect of PA on CVD risk and all-cause mortality, and the approach was complemented using conditional logistic regression models within a matched nested case–control design. A total of 5111 (19.2%) patients died during follow-up, which corresponds to 37.8% of the inactive group, 23.9% of the partially active group and 12.4% of the active group. CVD events occurred in 2362 (8.9%) patients, which corresponds to 11.6%, 10.1% and 7.6% of these groups. Compared with patients in the inactive group, and after matching and adjusting for confounders, the OR of having a CVD event was 0.84 (95% CI: 0.66–1.07) for the partially active group and 0.71 (95% CI: 0.56–0.91) for the active group. A slightly more pronounced gradient was obtained when focused on all-cause mortality, with ORs equal to 0.72 (95% CI: 0.61–0.85) and 0.50 (95% CI: 0.42–0.59), respectively. This study provides further evidence that physically active patients with T2D may have a reduced risk of CVD-related complications and all-cause mortality.

Factors associated with stroke among patients with type 2 diabetes mellitus in China: a propensity score matched study

OBJECTIVE

This study aimed to examine the prevalence of stroke and associated factors of stroke in patients with type 2 diabetes(T2DM) in China.

METHODS

Participants were 18,013 T2DM patients recruited with stratified random cluster sampling method from December 2013 to January 2014 in China. Propensity score matching was used to eliminate confounding effects between groups and logistic regression analysis was used to examine factors associated with stroke among T2DM patients.

RESULTS

Overall, the prevalence of stroke in the subjects with T2DM was 9.5%. After nearest neighbor matching, smoking (OR = 1.60, 95%CI: 1.26-2.03), hypertension (OR = 2.96, 95%CI: 2.55-3.43), dyslipidemia (OR = 2.00, 95%CI: 1.71-2.33), family history of stroke (OR = 2.02, 95%CI: 1.61-2.54), obesity (OR = 1.21, 95%CI: 1.01-1.45) and sleep duration < 6 h/day (OR = 1.44, 95%CI: 1.20-1.73) or > 8 h/day (OR = 1.22, 95%CI: 1.05-1.42) were positively associated with stroke, whereas drinking 1-3 days/week (OR = 0.64, 95%CI: 0.45-0.90) or daily (OR = 0.45, 95%CI: 0.33-0.60), effective exercise (OR = 0.65, 95%CI: 0.57-0.73) and underweight (OR = 0.30, 95%CI: 0.13-0.71) were negatively related to stroke. Besides, the risk of stroke increased substantially with accumulation of above seven modified risk factors. The odds ratio values of stroke in patients having ≥ 5 of the above seven risk factors was 14.39 (95% CI: 8.87-23.26).

CONCLUSIONS

The prevalence of stroke was high among T2DM in China. It is of great significance to strengthen comprehensive management of health-related behaviors including smoking cessation, moderate alcohol consumption, effective exercise, 6-8 h of sleep duration, keeping normal weight and the prevention of hypertension and dyslipidemia to have sustained beneficial effects on improvements of stroke risk factors.

Analysis of prevalence and influencing factors of stroke in elderly hypertensive patients: Based on the screening plan for the high-risk population of stroke in Jiading District, Shanghai

Background

The purpose of this study is to investigate and analyze the prevalence and influencing factors of stroke in hypertensive patients aged 60 and above in Jiading District, Shanghai.

Methods

The population-based study included 18,724 screened people with hypertension (age ≥ 60 years, 48.7% women). From 2016 to 2019, data on demographics, potential influencing factors and health status were collected through face-to-face interviews, physical examinations, and laboratory tests. Logistic multivariate logistic regression model was used to analyze the influencing factors associated with stroke.

Results

Among the object of study from 2016 to 2019, 2,025 patients were screened for stroke, with the overall prevalence rate of 10.82% (10.41%-11.23%). Multivariate adjusted model analysis showed that dyslipidemia (OR:1.31,95%CI:1.19–1.45), lack of exercise (OR:1.91,95%CI:1.32–2.76), atrial fibrillation [OR:1.49,95%CI:1.35–1.65), family history of stroke (OR:2.18,95%CI:1.6–2.88) were the significant independent influencing factors of stroke in hypertensive patients over 60 years old. When these four factors were combined, compared with participants without any of these factors, the multi-adjusted odds ratios (95% confidence interval) of risk of stroke for persons concurrently having one, two and three or more of these factors were 1.89 (1.67–2.13), 2.15 (1.86–2.47) and 6.84 (4.90–9.55), respectively (linear trend P < 0.001); after multivariate adjustment, the family history of stroke had additive interaction with lack of exercise [RERI = 1.08(0.22–1.94), AP = 0.19(0.04–0.35), S = 1.31(1.02–1.69)], dyslipidemia [RERI = 0.87(0.41–1.33), AP = 0.23(0.08–0.38), S = 1.46(1.04–2.05)].

Conclusion

The prevalence of stroke was high in hypertensive patients aged 60 and above in Jiading District, Shanghai. Dyslipidemia, lack of exercise, atrial fibrillation and family history of stroke were significantly associated with stroke in hypertensive population. Stroke risk can be increased especially when multiple factors coexisting, and family history of stroke combined with a lack of exercise or dyslipidemia.

Association of BMI with cardiovascular disease incidence and mortality in patients with type 2 diabetes mellitus: A systematic review and dose-response meta-analysis of cohort studies

AIMS

The relation of body mass index (BMI) with cardiovascular disease (CVD) and mortality has been extensively investigated in the general population but is less clear in individuals with type 2 diabetes mellitus (T2DM). We performed a meta-analysis of cohort studies to quantitatively evaluate the association of BMI with CVD incidence and mortality in patients with T2DM.

DATA SYNTHESIS

PubMed and Embase databases were searched for relevant cohort articles published up to June 8, 2020. Restricted cubic splines were used to evaluate the potential linear or non-linear dose-response associations. We identified 17 articles (21 studies) with 1,349,075 participants and 57,725 cases (49,354 CVD incidence and 8371 CVD mortality) in the meta-analysis. We found a linear association between BMI and risk of CVD incidence (P_{non-linearity} = 0.182); the pooled RR for CVD incidence was 1.12 (95% CI, 1.04-1.20) with a 5-unit increase in BMI. We found an overall nonlinear relationship between BMI and CVD mortality (P_{non-linearity} < 0.001). The lowest risk was at BMI about 28.4 kg/m², with increased mortality risk for higher BMI values; the RR with a 5-unit increase in BMI was 0.87 (95% CI, 0.79-0.96) and 1.11 (95% CI, 1.04-1.18) for BMI ≤28.4 kg/m² and BMI >28.4 kg/m², respectively.

CONCLUSIONS

In individuals with T2DM, BMI may have a positive linear association with risk of CVD incidence but a nonlinear association with CVD mortality. Our results can provide evidence for weight control and lifestyle intervention for preventing and managing cardiovascular disease in T2DM.